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@ARTICLE{Nagy:279194,
      author       = {Nagy, Tamas and Gonda, Xenia and Gezsi, Andras and Eszlari,
                      Nora and Hullam, Gabor and González-Colom, Rubèn and
                      Mäkinen, Hannu and Paajanen, Teemu and Torok, Dora and Gal,
                      Zsofia and Petschner, Peter and Cano, Isaac and Kuokkanen,
                      Mikko and Schmidt, Carsten O and Van der Auwera, Sandra and
                      Roca, Josep and Antal, Peter and Juhasz, Gabriella},
      title        = {{P}harmacological profiling of major depressive
                      disorder-related multimorbidity clusters.},
      journal      = {European neuropsychopharmacology},
      volume       = {96},
      issn         = {0924-977X},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {DZNE-2025-00722},
      pages        = {71 - 83},
      year         = {2025},
      abstract     = {We previously identified seven distinct multimorbidity
                      clusters associated with major depressive disorder through a
                      comprehensive analysis of 1.2 million individuals of
                      multiple cohorts. These clusters, characterized by unique
                      clinical, genetic, and psychiatric and somatic illness risk
                      profiles, implicate divergent treatment pathways and disease
                      management strategies. This study aims to deepen the
                      understanding of these clusters by analyzing drug
                      prescriptions, evaluating the effectiveness of
                      antidepressant treatment strategies, and identifying
                      potential markers for personalized medicine. Utilizing drug
                      prescription data in the format of ATC codes, we performed
                      epidemiological assessments, including multimorbidity
                      (number of diseases), polypharmacy (number of chemical
                      substances), and drug burden (number of prescriptions)
                      analyses across the clusters. We applied linear regression
                      models to assess strength and predictive capability of
                      cluster membership on various metrics, and logistic
                      regression to explore associations with treatment-resistant
                      depression. We also quantified and visualized common
                      antidepressant treatment sequences within each cluster. Our
                      findings indicate significant variations in polypharmacy and
                      drug burden across clusters, with distinct patterns emerging
                      that correlate with the clusters' profiles. Clusters liable
                      to multimorbidity have higher drug burden, even after
                      correction for number of diseases. Furthermore, the three
                      clusters with higher risk for MDD showed different
                      antidepressant treatment profiles; two required
                      significantly more antidepressant prescriptions and had a
                      higher risk for TRD. The detailed pharmacological profiling
                      presented in this study not only corroborates the initial
                      cluster definitions but also enhances our predictive
                      capabilities for treatment outcomes in MDD. By linking
                      pharmacological data with comorbidity profiles, we pave the
                      way for targeted therapeutic interventions.},
      keywords     = {Humans / Depressive Disorder, Major: drug therapy /
                      Depressive Disorder, Major: epidemiology / Antidepressive
                      Agents: therapeutic use / Multimorbidity / Male /
                      Polypharmacy / Female / Middle Aged / Cluster Analysis /
                      Adult / Aged / Antidepressants (Other) / Major depressive
                      disorder (Other) / Multimorbidity (Other) / Pharmacology
                      (Other) / Antidepressive Agents (NLM Chemicals)},
      cin          = {AG Grabe},
      ddc          = {610},
      cid          = {I:(DE-2719)5000001},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40483774},
      doi          = {10.1016/j.euroneuro.2025.05.007},
      url          = {https://pub.dzne.de/record/279194},
}